OVERVIEW
A global pharmaceutical manufacturing company required a criticality analysis and maintenance optimization project for three major production systems. Mipu’s data-driven approach combined FMECA methodology, field walkthroughs, and predictive maintenance feasibility studies to develop optimized maintenance plans with detailed technical instructions, delivering measurable improvements in maintenance effectiveness, asset reliability, and waste reduction.
INITIAL SITUATION
The pharmaceutical company needed to optimize maintenance strategies for three principal assets critical to its production operations. The existing maintenance approach lacked a structured criticality analysis, leading to maintenance resources being allocated without clear prioritization based on operational risk, reliability impact, and business objectives.
Historical maintenance data was available in the Maximo CMMS, but job plans required data cleaning and rationalization. The maintenance and reliability teams needed to clearly identify which components were driving production risk and where predictive maintenance could most effectively improve reliability and reduce unplanned downtime.
The challenge was further complicated by the need to align physical assets with P&ID documentation and CMMS records, a common issue in complex pharmaceutical plants where documentation often diverges from real field conditions over time.
THE CHALLENGE
The objective was to establish a risk-based maintenance strategy for three critical pharmaceutical systems through:
- Comprehensive criticality analysis using FMECA methodology
- Cleaning and rationalization of work order history and existing job plans
- Physical verification of assets against P&IDs and CMMS data
- Integration of predictive maintenance where technically and economically feasible
- Development of practical technical instructions for maintenance execution
The maintenance team needed actionable, data-driven maintenance plans that balanced reliability improvement with economic sustainability and waste reduction.
THE SOLUTION
Mipu implemented a structured workflow combining analytical rigor with hands-on field validation:
Data Collection & Cleaning:
Historical maintenance data, including work orders and job plans, was extracted and cleaned from Maximo. Technical documentation such as P&IDs and equipment manuals was consolidated. A preliminary “quick and dirty” RCM assessment provided initial prioritization insights.
Field Walkthrough:
On-site inspections verified asset conditions and locations, resolving inconsistencies between physical equipment, P&ID documentation, and CMMS records, ensuring the reliability of the criticality analysis.
FMECA Analysis:
A detailed Failure Mode, Effects and Criticality Analysis was performed, calculating Risk Priority Numbers (RPN) for each asset and failure mode. Pareto analysis highlighted the small number of assets responsible for the majority of operational and reliability risk.
Predictive Maintenance Integration:
Feasibility studies assessed multiple predictive maintenance technologies, including vibration analysis, infrared thermography, ultrasound, and oil analysis. Technical instructions were developed to support the implementation of condition-based maintenance on high-criticality assets.
Maintenance Optimization:
Maintenance job plans were revised based on criticality results. Proactive maintenance tasks and technical instructions were created to mitigate high-RPN failure modes, shifting maintenance effort toward activities with the greatest impact on system reliability.
Improvement Analysis:
“What-if” scenarios simulated the effect of proposed maintenance changes on failure criticality, supporting data-driven decisions on maintenance strategy and resource allocation.
RESULTS
The project delivered concrete results aligned with pharmaceutical reliability and maintenance objectives:
Risk Reduction:
- Criticality parameters and Risk Priority Numbers defined for all assets and failure modes
- Pareto analysis identified the most critical systems requiring focused maintenance attention
- “What-if” analyses demonstrated projected reductions in failure criticality through optimized maintenance strategies
Predictive Maintenance Implementation:
- Identification of economically viable predictive maintenance opportunities across multiple PdM technologies
- Technical instructions developed for vibration monitoring, thermography, and other condition-based techniques
- Cost-benefit analysis confirmed the potential for reliability improvement and waste reduction
Optimized Maintenance Plans:
- Revised job plans targeting high-criticality failure modes
- Clear technical instructions supporting proactive and predictive maintenance execution
- KPI framework established to monitor predictive maintenance performance over time
Economic Validation:
A comprehensive cost-benefit analysis quantified the economic impact of the optimized maintenance strategy, providing a clear ROI justification and supporting long-term reliability improvement.
The project enabled the pharmaceutical company to move toward a fully risk-based maintenance approach, integrating predictive maintenance technologies where they deliver maximum value in terms of reliability, operational efficiency, and waste reduction.
